Contextual real-time content highlighting on shared screens

ABSTRACT

A method, computer system, and a computer program product for contextual digital content highlighting is provided. Discussion is monitored between a plurality of parties in conjunction with a digital presentation and a context of the monitored discussion is then identified. Then a most relevant portion of displayed digital content associated with the presentation is identified based on the identified context and highlighting then is applied to the identified most relevant portion of the displayed content.

BACKGROUND

The present invention relates generally to the field of computing, andmore particularly to dynamic intelligent interface manipulation.

Electronic meetings and presentations are now used with increasingfrequency. During these meetings a common scenario occurs when aspecific topic is discussed between two or more parties related tocontent displayed on a screen. In these situations, contextidentification from the discussion that is relevant to the presentationcontent may ensure that the focus of the presentation or discussionshifts back to the relevant content of the presentation.

SUMMARY

According to one exemplary embodiment, a method for contextual digitalcontent highlighting is provided. Discussion is monitored between aplurality of parties in conjunction with a digital presentation and acontext of the monitored discussion is then identified. Then a mostrelevant portion of displayed digital content associated with thepresentation is identified based on the identified context andhighlighting then is applied to the identified most relevant portion ofthe displayed content. A computer system and computer program productcorresponding to the above method are also disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings. The various features of the drawings arenot to scale as the illustrations are for clarity in facilitating oneskilled in the art in understanding the invention in conjunction withthe detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to atleast one embodiment;

FIG. 2 is an operational flowchart illustrating a process for contextualhighlighting according to at least one embodiment;

FIGS. 3A-C depict exemplary contextual highlighting applied to apresentation slide according to at least one embodiment;

FIG. 4 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 5 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1, in accordance with anembodiment of the present disclosure; and

FIG. 6 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 5, in accordance with an embodiment of thepresent disclosure.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. This invention may, however, be embodied inmany different forms and should not be construed as limited to theexemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope of this invention to thoseskilled in the art. In the description, details of well-known featuresand techniques may be omitted to avoid unnecessarily obscuring thepresented embodiments.

As described above, electronic meetings and presentations are now usedwith increasing frequency. During these meetings a common scenariooccurs when a specific topic is discussed between two or more partiesrelated to content displayed on a screen. In these situations, contextidentification from the discussion that is relevant to the presentationcontent may ensure that the focus of the presentation or discussionshifts back to the relevant content of the presentation.

Therefore, it may be advantageous to, among other things, provide a wayto highlight or otherwise indicate in real-time which portion of contentdisplayed during a presentation relates to the current discussion suchthat participants may quickly ascertain the relevant portion of thedisplayed content. It may be further advantageous to, in someembodiments, validate information in real-time that is displayed andrelevant to the current discussion, and convey the validity to theparticipants in order to more quickly resolve tangential discussions,thereby allowing the meeting or presentation to proceed.

The following described exemplary embodiments provide a system, methodand program product for real-time contextual cursor highlighting ofdisplayed content. As such, the present embodiment has the capacity toimprove the technical field of user interfaces by intelligentlyhighlighting relevant content in real-time based on ongoinginteractions. More specifically, the current topic of discussion betweentwo or more parties is determined and compared with the content that isdisplayed currently. Displayed content within the greatest similarity tothe current discussion topic is then identified. The identified contentwithin the displayed content is then highlighted. According to someembodiments, the validity of the identified content may also bedetermined and the resulting validity may be indicated via on-screenidentifier.

Reference is made with respect to certain embodiments in the context ofvirtual meetings. Virtual meetings as used herein refers to meetings oftwo or persons (i.e., participants or attendees) online through digitalmeans instead of traditional physical meetings where the participantsmeet face-to-face in the same room. Virtual meetings may include digitaltransmission of data over a network between computers or otherelectronic devices including audio, video, text, images, and so on, suchthat the participants may experience and interact during a presentationor meeting similar to a traditional physical meeting. The term virtualmeeting may be used interchangeably with online meeting, electronicmeeting, or video conferencing.

As used herein, presentation software refers to software, applications,other executable code which causes an electronic device, such as acomputer, to display presentations on a screen. For instance, a slidedeck used in a presentation may be displayed on the presenter's screenusing the presentation software. For example, presentation software mayinclude PowerPoint® (PowerPoint and all PowerPoint-based trademarks andlogos are trademarks or registered trademarks of the MicrosoftCorporation and/or its affiliates).

As used herein, meeting software refers to software, applications, otherexecutable code which causes an electronic device, such as a computer,to conduct a virtual meeting by sharing the screen of a meetingparticipant (e.g., speaker) with the rest of the meeting attendees anddistribute audio, video, or other data to facilitate attendeeparticipation in the meeting or presentation. An example of meetingsoftware may include Webex® (Webex and all Webex-based trademarks andlogos are trademarks or registered trademarks of the Cisco Systems, Inc.and/or its affiliates).

Referring to FIG. 1, an exemplary networked computer environment 100 inaccordance with one embodiment is depicted. The networked computerenvironment 100 may include a computer 102 with a processor 104 and adata storage device 106 that is enabled to run a software program 108and a contextual highlighting program 110 a. The networked computerenvironment 100 may also include a server 112 that is enabled to run acontextual highlighting program 110 b that may interact with a database114 and a communication network 116. The networked computer environment100 may include a plurality of computers 102 and servers 112, only oneof which is shown. The communication network 116 may include varioustypes of communication networks, such as a wide area network (WAN),local area network (LAN), a telecommunication network, a wirelessnetwork, a public switched network and/or a satellite network. It shouldbe appreciated that FIG. 1 provides only an illustration of oneimplementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environments may be made based on designand implementation requirements.

The client computer 102 may communicate with the server computer 112 viathe communications network 116. The communications network 116 mayinclude connections, such as wire, wireless communication links, orfiber optic cables. As will be discussed with reference to FIG. 4,server computer 112 may include internal components 902 a and externalcomponents 904 a, respectively, and client computer 102 may includeinternal components 902 b and external components 904 b, respectively.Server computer 112 may also operate in a cloud computing service model,such as Software as a Service (SaaS), Platform as a Service (PaaS), orInfrastructure as a Service (IaaS). Server 112 may also be located in acloud computing deployment model, such as a private cloud, communitycloud, public cloud, or hybrid cloud. Client computer 102 may be, forexample, a mobile device, a telephone, a personal digital assistant, anetbook, a laptop computer, a tablet computer, a desktop computer, orany type of computing devices capable of running a program, accessing anetwork, and accessing a database 114. According to variousimplementations of the present embodiment, the contextual highlightingprogram 110 a, 110 b may interact with a database 114 that may beembedded in various storage devices, such as, but not limited to acomputer/mobile device 102, a networked server 112, or a cloud storageservice.

According to the present embodiment, a user using a client computer 102or a server computer 112 may use the contextual highlighting program 110a, 110 b (respectively) to identify and highlight relevant contentdisplayed on a shared screen in real-time based on the topic ofdiscussion. The contextual highlighting method is explained in moredetail below with respect to FIGS. 2 and 3A-C.

Referring now to FIG. 2, an operational flowchart illustrating theexemplary contextual highlighting process 200 used by the contextualhighlighting program 110 a and 110 baccording to at least one embodimentis depicted.

At 202 a presentation or meeting begins. The presentation, virtualmeeting, or the like may include content displayed to participants oraudience members. Displayed content may be presented on device screenconnected to a computer 102, projected onto a screen, displayed inaugmented reality devices, and so on. Displayed content may includetext, tables, charts, images, video clips, audio clips, and the likerelated to a presentation or discussion (e.g., a work meeting). In otherwords, the subject matter of the displayed content is related to thetopic of discussion and may be used in conjunction with the discussion.

A presenter, meeting leader, or the like may utilize presentationsoftware (e.g., software program 108) to display content to thepresentation participants. For example, a collection of orderedpresentation slides may be prepared, loaded, and then displayed by thepresentation software running on a computer 102 that outputs one slideat a time to a screen that may be shared with the participants. Theshared screen may, for example, include a projection screen to anaudience physically together in one location, or each participant mayreceive via a communication network 116 the content of the current slideat their individual computer 102 that displays the slide content ontheir individual screen in virtual meetings, or some combinationthereof. Additionally, microphones, cameras, and other relevant sensorsmay be used to monitor the discussion occurring concurrently during thepresentation or meeting. The contextual highlighting process 200 maydetermine the meeting has started once a trigger indicating thepresentation has begun is identified. For instance, a trigger mayinclude when the first slide is displayed to the participants or byusing natural language processing (NLP) to identify predeterminedphrases spoken at the start of a meeting (e.g., “Let's go ahead andbegin”) and obtained via a microphone.

Next, at 204, discussion during a presentation is analyzed. As describedabove, microphones and other sensors may be used to monitor the speechof participants. In some embodiments, vocalized speech may not be usedby some or all participants and instead text messages or othernon-verbal communications may be used by participants during apresentation to interact with the speaker or presenter. As such, thecontextual highlighting process 200 may monitor a combination of verbaland non-verbal communication during a discussion. Verbal communicationsmay be monitored using, for example, microphones connected to a computer102. The audio data may be obtained from a microphone by the meetingsoftware for transmission to the participants. In embodiments, thecontextual highlighting process 200 may interface (e.g., via anapplication programming interface (API)) with the meeting software toobtain the audio data and process the audio data using NLP techniques.In instances when text-based discussion may occur, the contextualhighlighting process 200 may interface (e.g., via an API) with themeeting software or messaging applications to obtain questions,comments, or other communication from other participants or presentersfor analysis.

Then, at 206, the contextual highlighting process 200 determines if twoor more parties are involved in the discussion. The parties involved inthe discussion may include one or more presenters or participants. In atraditional physical meeting, determining two or more parties areinvolved in a discussion may include analysis of audio data captured viamicrophone. This analysis of audio data may use NLP techniques toidentify words and phrases indicating a conversation between twoparties. In some embodiments, vocal characteristics may be identifiedand then used by machine learning models to determine that two or moredifferent people are speaking to each other. In virtual meetingsettings, the contextual highlighting process 200 may interface with themeeting software to identify each person speaking or non-verballycommunicating since the meeting software will identify the parties whoare communicating based on the users signed in and the devicestransmitting the signed in user's communications.

For example, in a virtual meeting discussing financial market trends,the speaker discusses the Nifty Index. A slide is displayed on a sharedscreen that proposes the Nifty Index will increase over the next tendays. As the speaker is making a point with respect to the Nifty Index,a participant P verbally asks a question regarding recent investmentfrom a social media Company X in Company R and the impact of the futuremarket capitalization of Company R. The contextual highlighting process200 will interface with the meeting software to determine that one partyis the speaker by detecting audio from the speaker's microphone and alsodetermines that participant P is a party to the discussion by detectingaudio from P's microphone.

If the contextual highlighting process 200 determined that there are nottwo or more parties are involved in a discussion at 206, then thecontextual highlighting process 200 returns to 204 to analyze thediscussion.

However, if the contextual highlighting process 200 determined that twoor more parties are involved in a discussion at 206, then the context ofthe discussion is identified at 208. In embodiments, semantic analysismay be performed on the discussion data (e.g., audio data from amicrophone) to ascertain the meaning conveyed in the current discussion.As described previously, in some instances a combination of audio dataand non-verbal data may be analyzed semantically to determine thesubject or topic of discussion thereby establishing the context of thediscussion. In some embodiments, machine learning algorithms may be usedto classify the discussion data based on topics. Other semantic analysistechniques may identify sentiments, intents, and so on. Additionally,semantic analysis may include keyword or entity extraction. After thesemantic analysis is performed, additional data relating to theidentified topics and keywords may be collected by searching knowledgebases, the Internet, and the like.

Continuing the previous example, the audio data from the speaker andparticipant P is analyzed using a supervised machine learning algorithmand the contextual highlighting process 200 receives keywords andphrases such as “Company R,” “investment by Company X,” and “futuremarket capitalization” extracted by the supervised machine learningalgorithm. After the keywords and phrases are extracted, the contextualhighlighting process 200 searches for information related to thekeywords via the Internet and determines that Company R is a part of theNifty Index. Additionally, the contextual highlighting process 200determines that Company X is a profitable company and that investmentsby profitable companies typically increases market capitalization of theinvestee. Moreover, the contextual highlighting process 200 determinesthat if Company R′s market capitalization increases, the Nifty Indexwill be positively impacted as well since Company R is a constituentcompany of the Nifty.

Next, at 210, the most relevant displayed content is determined. Inembodiments, the contextual highlighting process 200 may interface withthe presentation software via an API to retrieve the displayed content.The displayed content may include text, images, and so on. The displayedcontent may be the content contained in the currently displayed slidefrom a presentation. In some embodiments, the displayed content mayinclude previously displayed content. For example, a speaker may notgive a participant the chance to ask a question regarding the contentuntil the speaker has moved on to the next slide. As such, someembodiments may obtain content previously displayed. In someembodiments, the previously displayed content may be limited by athreshold amount of time since the slide was displayed (e.g., 2minutes), a threshold number of previously displayed slides (e.g., 3slides previous), or a percentage of the presentation previouslydisplayed (e.g., 10% of 100 slides, thus 10 slides previous).

To determine the most relevant content from the displayed content,semantic analysis may be performed on the displayed content, similar tothe semantic analysis of the discussion described above with respect to208. Consequently, a machine learning model may be used to analyze thedisplayed content to identify keywords and phrases and other semanticcues. Since the displayed content may include images, tables, charts,video and other visual data, the semantic analysis may involvepreliminary image analysis to generate textual representations of visualdata contained within the displayed content. The textual representationsof the visual data may then be grouped with the textual data from thedisplayed content and input into machine learning models to identifykeywords or phrases. The textual data representing the displayed contentmay be broken down or tokenized into portions such as by sentence,clauses within a sentence, words, list items, individual images, and soon.

Once the displayed content has been semantically analyzed, a similarityalgorithm may be used to compare the context of the discussionidentified at 208 to the semantics of the displayed content to find theportions of the displayed content that most closely match the discussioncontext. Semantic similarity algorithms may output a score indicatinghow similar each portion of the displayed content is to the discussioncontext (e.g., normalized from 0 to 1 with 1 being close similarity).According to at least one embodiment, once the similarity scores arecalculated for each portion of displayed content, the portions may besorted and ranked by similarity scores. The portion having the highestsimilarity score may then be selected as the most relevant displayedcontent.

Continuing the previous example, and as depicted in FIG. 3A, thedisplayed content in the presentation slide 300 includes a graph 302showing the Nifty Index historical values accompanied by text 304stating “Looking at the Trend Line, the Nifty Index will go up 10% inthe coming 10 sessions. Keep an eye on Nifty shares for next 10 days. Itis a great investment opportunity.” The contextual highlighting process200 may obtain the displayed content (i.e., graph 302 and text 304) fromthe presentation software. Thereafter, the contextual highlightingprocess 200 may identify visual data (i.e., graph 302) and perform imageanalysis to generate a textual representation of the graph 302. The text304 may be broken down into portions based on clauses. The textualrepresentation of the graph 302 along with the clauses from the text 304are then semantically analyzed and compared to the discussion contextidentified previously. After computing similarity scores for eachportion of the displayed content, the scores are ranked from highest tolowest. The portion of displayed content with the highest similarityscore is the clause “Nifty Index will go up 10% in the coming 10sessions.” Thus, the clause “Nifty Index will go up 10% in the coming 10sessions” is determined to be the most relevant portion of the displayedcontent.

Then, at 212, the most relevant content within the displayed content ishighlighted on screen. According to at least one embodiment, the portionof the displayed content identified previously at 210 is highlighted bycommunicating with the presentation software, (e.g., via an appropriateAPI call) and thereafter the presentation may respond to thecommunication from the contextual highlighting process 200 byhighlighting the displayed content portion. Highlighting may include anycombination of visual or other alterations to the displayed content todistinguish the portion of displayed content. For example, highlightingmay include applying a colored background to text, font style changes(such as from Times New Roman to Calibri), font size changes, font colorchanges, bolding, italicizing, underlining, static or animated arrowspointing to the relevant content, and so on.

In embodiments that search previous slides for relevant content asdescribed above at 210, highlighting the relevant content may includecommunicating with the presentation software to go back in the slidedeck to display the correct past slide which contains the relevantcontent and apply the highlighting to the relevant content. As such, therelevant content is both displayed on screen and highlighted inreal-time during the discussion despite the relevant content being on adifferent slide from the one initially displayed at the time thediscussion started.

Continuing the previous example, and as depicted in FIG. 3B, the clause“Nifty Index will go up 10% in the coming 10 sessions” was determined tobe the most relevant portion 306 of the displayed content. Thereafter,the contextual highlighting process 200 requests, via an API call, thatthe presentation software highlight the relevant portion 306 which isthe clause “Nifty Index will go up 10% in the coming 10 sessions.” Asshown in FIG. 3B, according to this example, the identified relevantportion 306 is highlighted by applying bold and underline to the text ofthe relevant portion 306.

At 214, the contextual highlighting process 200 determines if thediscussion of the topic is continuing. In embodiments, the discussionbetween the two or more parties in the meeting or presentation maycontinue to be analyzed after applying highlighting to the relevantportion 306 of the displayed content as described above at 212. Thecontextual highlighting process 200 may determine if the discussionbetween two or more parties continues by monitoring and analyzing thediscussion as described above previously at 206. In embodiments, thetopic of discussion may be identified in the manner described previouslyat 208 and the topic compared with the topic determined previously at208 to determine if the same topic is continuing to be discussed. Inaddition to, or alternatively, the current discussion topic may becompared to the semantics of the relevant portion 306 to determine ifthe discussion is still closely related to the highlighted relevantportion 306.

In some embodiments, the contextual highlighting process 200 may alsodetermine if the discussion includes indications that one or more of theparties is expressing doubts or questions regarding the validity of therelevant portion 306. Various NLP techniques used in a supervisedmachine learning model may be employed to determine if doubts orquestions are raised with respect to the relevant portion 306 by, forexample, identifying predetermined words or phrases, analyzing tone ofvoice, monitoring facial expression or body language of a participantvia a camera, and so on.

Continuing the previous example, after highlighting the relevant portion306, another participant P2 states that “Company R′s value would notchange enough to cause the Nifty Index to increase 10% in 10 days.”Based on the analysis of the discussion, the topic of discussion remainsrelated to the increase of the Nifty Index over the next 10 days.Moreover, participant P2 has expressed doubts regarding the pointsraised in the discussion and the corresponding content highlighted inthe relevant portion 306 which are identified by NLP analysis of P2'sstatement.

If the contextual highlighting process 200 determines that thediscussion has moved on to a different topic, or according to someembodiments, no discussion regarding the validity of the topic isidentified at 214 then the contextual highlighting process 200 returnsto 204 to analyze the discussion.

However, if the contextual highlighting process 200 determines that thediscussion has continued on the same topic, or according to someembodiments, if the contextual highlighting process 200 also determinedthat disagreement regarding the validity of the topic are identified at214, then the validity of the relevant content 306 is verified at 216.According to at least one embodiment, the contextual highlightingprocess 200 may utilize a separate service or program to verify thevalidity of content. For instance, the contextual highlighting process200 may transmit (e.g., via communication network 116) the content(e.g., the relevant content 306) to be verified to a validation servicewhich may, in some implementations, search the Internet, other knowledgebase, or some other information sources to determine the validity of therelevant content 306 the contextual highlighting process 200transmitted. In response to the contextual highlighting process 200verification request, the validation service may return a validity scoreindicating the determined validity of the relevant content 306.Thereafter, the contextual highlighting process 200 may compare thevalidity score to a predetermined threshold to determine if the relevantcontent 306 is valid or not valid. In other embodiments, the validityservice may make the determination of validity and then transmit aresponse to the contextual highlighting process 200 indicating therelevant content 306 is valid or not valid.

Continuing the previous example, the contextual highlighting process 200transmits the relevant content 306 clause stating “Nifty Index will goup 10% in the coming 10 sessions” to a validation service whichdetermines that based on the current trends, there is sufficientconfidence the relevant content 306 is valid to return a result of“valid” to the contextual highlighting process 200.

Then, at 218, a validity indicator is added adjacent to the relevantcontent 306. In embodiments, the contextual highlighting process 200 maycommunicate with the presentation software (e.g., via an API call) torequest a validity indicator be added to the displayed content (e.g.,presentation slide 300). The contextual highlighting process 200 mayprovide the appropriate indicator that is then placed on the slide bythe presentation software. In other embodiments, the presentationsoftware may place its own indicator in response to the contextualhighlighting process 200 request together with the validity result(i.e., valid or not valid). The validity indicator may take variousforms of static, animated, audio, or other indications. For example, theindicator may be a thumbs up (valid) or thumbs down (not valid), an “x”(not valid) or checkmark (valid), and the like. In other embodiments,alternatively or in addition to the indicators described above, thehighlighted relevant content 306 may be further altered to indicatevalidity. For example, if the relevant content is verified as valid, thetext of the relevant content 306 may be changed from black in color togreen. Thereafter, the contextual highlighting process 200 returns tostep 204 to continue analyzing the discussion.

Continuing the previous example, and with reference to FIG. 3C, therelevant content 306 was verified as valid. Thereafter, the contextualhighlighting process 200 communicates with the presentation softwarerequesting that a validity indicator that indicates the relevant content306 is valid be added to the presentation slide 300. The presentationsoftware then adds a thumbs up validity indicator 308 adjacent to therelevant content 306 as depicted to indicate that the relevant content306 is valid.

It may be appreciated that FIGS. 2 and 3A-C provide only an illustrationof one embodiment and do not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted embodiment(s) may be made based on design and implementationrequirements. For example, according to alternative embodiments thecontextual highlighting process 200 may be integrated into thepresentation software or the meeting software, thus communications maybe internalized within the software. This integration may, in someembodiments, be accomplished by implementing the contextual highlightingprocess 200 as a plugin to the presentation or meeting software. In someembodiments, the presentation and meeting software may be combined intoa single application that implements the contextual highlighting process200.

As described in embodiments above, the contextual highlighting program110 a and 110 b may improve the functionality of a computer or othertechnology by providing content highlighting in real-time to contentdisplayed on shared screens that relates to the current discussion suchthat participants may quickly ascertain the relevant portion of thedisplayed content in view of the current discussion. Moreover, thevalidity of the content may be determined and indicated in real-timeduring a discussion to inform the participants of the validity of thehighlighted content and resolve tangential discussion quickly therebyallowing the presentation or meeting to proceed without undue delay.

FIG. 4 is a block diagram 900 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment of the present invention. It should be appreciated that FIG.4 provides only an illustration of one implementation and does not implyany limitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironments may be made based on design and implementationrequirements.

Data processing system 902, 904 is representative of any electronicdevice capable of executing machine-readable program instructions. Dataprocessing system 902, 904 may be representative of a smart phone, acomputer system, PDA, or other electronic devices. Examples of computingsystems, environments, and/or configurations that may represented bydata processing system 902, 904 include, but are not limited to,personal computer systems, server computer systems, thin clients, thickclients, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, network PCs, minicomputer systems, anddistributed cloud computing environments that include any of the abovesystems or devices.

User client computer 102 and network server 112 may include respectivesets of internal components 902 a, b and external components 904 a, billustrated in FIG. 4. Each of the sets of internal components 902 a, bincludes one or more processors 906, one or more computer-readable RAMs908 and one or more computer-readable ROMs 910 on one or more buses 912,and one or more operating systems 914 and one or more computer-readabletangible storage devices 916. The one or more operating systems 914, thesoftware program 108, and the contextual highlighting program 110 a inclient computer 102, and the contextual highlighting program 110 b innetwork server 112, may be stored on one or more computer-readabletangible storage devices 916 for execution by one or more processors 906via one or more RAMs 908 (which typically include cache memory). In theembodiment illustrated in FIG. 4, each of the computer-readable tangiblestorage devices 916 is a magnetic disk storage device of an internalhard drive. Alternatively, each of the computer-readable tangiblestorage devices 916 is a semiconductor storage device such as ROM 910,EPROM, flash memory or any other computer-readable tangible storagedevice that can store a computer program and digital information.

Each set of internal components 902 a, b also includes a R/W drive orinterface 918 to read from and write to one or more portablecomputer-readable tangible storage devices 920 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the softwareprogram 108 and the contextual highlighting program 110 a and 110 b canbe stored on one or more of the respective portable computer-readabletangible storage devices 920, read via the respective R/W drive orinterface 918 and loaded into the respective hard drive 916.

Each set of internal components 902 a, b may also include networkadapters (or switch port cards) or interfaces 922 such as a TCP/IPadapter cards, wireless wi-fi interface cards, or 3G or 4G wirelessinterface cards or other wired or wireless communication links. Thesoftware program 108 and the contextual highlighting program 110 a inclient computer 102 and the contextual highlighting program 110 b innetwork server computer 112 can be downloaded from an external computer(e.g., server) via a network (for example, the Internet, a local areanetwork or other, wide area network) and respective network adapters orinterfaces 922. From the network adapters (or switch port adaptors) orinterfaces 922, the software program 108 and the contextual highlightingprogram 110 a in client computer 102 and the contextual highlightingprogram 110 b in network server computer 112 are loaded into therespective hard drive 916. The network may comprise copper wires,optical fibers, wireless transmission, routers, firewalls, switches,gateway computers and/or edge servers.

Each of the sets of external components 904 a, b can include a computerdisplay monitor 924, a keyboard 926, and a computer mouse 928. Externalcomponents 904 a, b can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 902 a, b also includes device drivers930 to interface to computer display monitor 924, keyboard 926 andcomputer mouse 928. The device drivers 930, R/W drive or interface 918and network adapter or interface 922 comprise hardware and software(stored in storage device 916 and/or ROM 910).

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, executed concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be executed in thereverse order, depending upon the functionality involved. It will alsobe noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as Follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as Follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as Follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 5, illustrative cloud computing environment 1000is depicted. As shown, cloud computing environment 1000 comprises one ormore cloud computing nodes 100 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 1000A, desktop computer 1000B, laptopcomputer 1000C, and/or automobile computer system 1000N may communicate.Nodes 100 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 1000to offer infrastructure, platforms and/or software as services for whicha cloud consumer does not need to maintain resources on a localcomputing device. It is understood that the types of computing devices1000A-N shown in FIG. 5 are intended to be illustrative only and thatcomputing nodes 100 and cloud computing environment 1000 can communicatewith any type of computerized device over any type of network and/ornetwork addressable connection (e.g., using a web browser).

Referring now to FIG. 6, a set of functional abstraction layers 1100provided by cloud computing environment 1000 is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 1102 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 1104;RISC (Reduced Instruction Set Computer) architecture based servers 1106;servers 1108; blade servers 1110; storage devices 1112; and networks andnetworking components 1114. In some embodiments, software componentsinclude network application server software 1116 and database software1118.

Virtualization layer 1120 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers1122; virtual storage 1124; virtual networks 1126, including virtualprivate networks; virtual applications and operating systems 1128; andvirtual clients 1130.

In one example, management layer 1132 may provide the functionsdescribed below. Resource provisioning 1134 provides dynamic procurementof computing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 1136provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 1138 provides access to the cloud computing environment forconsumers and system administrators. Service level management 1140provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 1142 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 1144 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 1146; software development and lifecycle management 1148;virtual classroom education delivery 1150; data analytics processing1152; transaction processing 1154; and contextual highlighting 1156. Acontextual highlighting program 110 a, 110 b provides a way to identifyrelevant content displayed on a shared screen in real-time based oncontextual cues and highlight the relevant content.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a,” “an,” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises,”“comprising,” “includes,” “including,” “has,” “have,” “having,” “with,”and the like, when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but does not preclude the presence or addition of one ormore other features, integers, steps, operations, elements, components,and/or groups thereof

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer-implemented method for contextualdigital content highlighting, the method comprising: monitoring adiscussion between a plurality of parties in conjunction with apresentation; identifying a context of the monitored discussion;identifying a most relevant portion of a displayed content associatedwith the presentation based on the identified context; and applyinghighlighting to the identified most relevant portion.
 2. Thecomputer-implemented method of claim 1, further comprising: determiningthat the plurality of parties are having the discussion.
 3. Thecomputer-implemented method of claim 1, further comprising: determiningthat the monitored discussion is continuing; in response to determiningthat the monitored discussion is continuing, determining a validity ofthe identified most relevant portion; and displaying a validityindicator adjacent to the identified most relevant portion based on thedetermined validity.
 4. The computer-implemented method of claim 1,wherein the applied highlighting is selected from the group consistingof a font size, a font color, a text background color, a font style, astatic arrow, and a dynamic arrow.
 5. The computer-implemented method ofclaim 1, wherein identifying the most relevant portion of the displayedcontent associated with the presentation based on the identified contextcomprises tokenizing text within the displayed content to generate aplurality of content portions.
 6. The computer-implemented method ofclaim 5, wherein identifying the most relevant portion of the displayedcontent associated with the presentation based on the identified contextcomprises generating a similarity score for each portion within theplurality of content portions by comparing the identified context toeach content portion and selecting a content portion having a highestsimilarity score relative to a remainder of content portions within theplurality of content portions.
 7. The computer-implemented method ofclaim 5, wherein the displayed content comprises one or more images,wherein image analysis is performed on the one or more images togenerate textual image representations describing each of the one ormore images in a textual form, and wherein the generated textual imagerepresentations are added to the plurality of content portions.
 8. Acomputer system for contextual digital content highlighting, comprising:one or more processors, one or more computer-readable memories, one ormore computer-readable tangible storage media, and program instructionsstored on at least one of the one or more computer-readable tangiblestorage media for execution by at least one of the one or moreprocessors via at least one of the one or more computer-readablememories, wherein the computer system is capable of performing a methodcomprising: monitoring a discussion between a plurality of parties inconjunction with a presentation; identifying a context of the monitoreddiscussion; identifying a most relevant portion of a displayed contentassociated with the presentation based on the identified context; andapplying highlighting to the identified most relevant portion.
 9. Thecomputer system of claim 8, further comprising: determining that theplurality of parties are having the discussion.
 10. The computer systemof claim 8, further comprising: determining that the monitoreddiscussion is continuing; in response to determining that the monitoreddiscussion is continuing, determining a validity of the identified mostrelevant portion; and displaying a validity indicator adjacent to theidentified most relevant portion based on the determined validity. 11.The computer system of claim 8, wherein the applied highlighting isselected from the group consisting of a font size, a font color, a textbackground color, a font style, a static arrow, and a dynamic arrow. 12.The computer system of claim 8, wherein identifying the most relevantportion of the displayed content associated with the presentation basedon the identified context comprises tokenizing text within the displayedcontent to generate a plurality of content portions.
 13. The computersystem of claim 12, wherein identifying the most relevant portion of thedisplayed content associated with the presentation based on theidentified context comprises generating a similarity score for eachportion within the plurality of content portions by comparing theidentified context to each content portion and selecting a contentportion having a highest similarity score relative to a remainder ofcontent portions within the plurality of content portions.
 14. Thecomputer system of claim 12, wherein the displayed content comprises oneor more images, wherein image analysis is performed on the one or moreimages to generate textual image representations describing each of theone or more images in a textual form, and wherein the generated textualimage representations are added to the plurality of content portions.15. A computer program product for contextual digital contenthighlighting, comprising a computer-readable storage medium havingprogram instructions embodied therewith, the program instructionsexecutable by a processor to cause the processor to perform a methodcomprising: monitoring a discussion between a plurality of parties inconjunction with a presentation; identifying a context of the monitoreddiscussion; identifying a most relevant portion of a displayed contentassociated with the presentation based on the identified context; andapplying highlighting to the identified most relevant portion.
 16. Thecomputer program product of claim 15, further comprising: determiningthat the plurality of parties are having the discussion.
 17. Thecomputer program product of claim 15, further comprising: determiningthat the monitored discussion is continuing; in response to determiningthat the monitored discussion is continuing, determining a validity ofthe identified most relevant portion; and displaying a validityindicator adjacent to the identified most relevant portion based on thedetermined validity.
 18. The computer program product of claim 15,wherein the applied highlighting is selected from the group consistingof a font size, a font color, a text background color, a font style, astatic arrow, and a dynamic arrow.
 19. The computer program product ofclaim 15, wherein identifying the most relevant portion of the displayedcontent associated with the presentation based on the identified contextcomprises tokenizing text within the displayed content to generate aplurality of content portions.
 20. The computer program product of claim19, wherein identifying the most relevant portion of the displayedcontent associated with the presentation based on the identified contextcomprises generating a similarity score for each portion within theplurality of content portions by comparing the identified context toeach content portion and selecting a content portion having a highestsimilarity score relative to a remainder of content portions within theplurality of content portions.